1 Todo
2 Load some libraries
3 Import the data
4 Overview
5 Export csv file (gzipped)
6 PM10
7 O3
8 Number of missing observations by stazione, year and month (PM10)
9 Highcharter
10 Vegalite
11 Dygraph
12 Dygraph, trimmed values
13 rbokeh
14 Maximum per year (PM10)
15 Days per year with a PM10 value over 50 mg/m3 (max. 35 excedances allowed per year)
16 Mappa stazioni

1 Todo

2 Load some libraries

library('stringr')
library('tidyr')
library('ggplot2')
library('dplyr')
library('highcharter')
library('vegalite')
library('lubridate')
library('leaflet')
library('dygraphs')
library('xts')
library('rbokeh')

ma_theme <- function(base_size=12, base_family="helvetica") {
              theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
                    legend.position = 'bottom')
}

split_names <- function(m) {
   stazione <- zz$stazione[which(str_detect(m, zz$stazione))]
   inq      <- zz$inq[which(str_detect(m, zz$inq))]
   tdm      <- zz$tdm[which(str_detect(m, zz$tdm))]
   out <- paste(stazione, inq, tdm, sep = "__")
   out
}

# tdm -> tipo di misura

zz <- read.table(header = TRUE, sep = ";", text = "
stazione;inq;tdm
partinico;SO2;Max_orario
sasol;CO;Media_24_h
enna;NO2;Max_media_8h
ex_autoparco;O3;NA
trapani;Benzene;NA
termini;PM10;NA
cda_gabbia;PM2.5;NA
termica_milazzo;NA;NA
boccetta;NA;NA
misterbianco;NA;NA
megara;NA;NA
lab_mobile1;NA;NA
porto_empedocle;NA;NA
villa_augusta;NA;NA
parcheggio_agip;NA;NA
")

zz %>% knitr::kable()

stazione	inq	tdm
partinico	SO2	Max_orario
sasol	CO	Media_24_h
enna	NO2	Max_media_8h
ex_autoparco	O3	NA
trapani	Benzene	NA
termini	PM10	NA
cda_gabbia	PM2.5	NA
termica_milazzo	NA	NA
boccetta	NA	NA
misterbianco	NA	NA
megara	NA	NA
lab_mobile1	NA	NA
porto_empedocle	NA	NA
villa_augusta	NA	NA
parcheggio_agip	NA	NA

3 Import the data

# http://www.arpa.sicilia.it/storage/MAria_report.xls
x <- read.csv("http://blog.spaziogis.it/static/ods/data/mariareport/MAria_report.csv")

colnames(x) <- c("date", unlist(lapply(colnames(x), split_names)))

xm <- gather(x, variable, value, -date)
#xm$variable <- as.character(xm$variable)

u <- data.frame(do.call("rbind", strsplit(xm$variable, "__")), stringsAsFactors = FALSE)
colnames(u) <- c("stazione", "inq", "tdm")
fin <- data.frame(u, date = xm$date, value = xm$value)

knitr::kable(head(fin))

stazione	inq	tdm	date	value
partinico	SO2	Max_orario	2013-01-01	2.227614
partinico	SO2	Max_orario	2013-01-02	1.331977
partinico	SO2	Max_orario	2013-01-03	4.150291
partinico	SO2	Max_orario	2013-01-04	7.960566
partinico	SO2	Max_orario	2013-01-05	3.602461
partinico	SO2	Max_orario	2013-01-06	1.674221

knitr::kable(tail(fin))

	tdm	date	value
123511	Media_24_h	2016-08-01	0.0984469
123512	Media_24_h	2016-08-02	0.0175628
123513	Media_24_h	2016-08-03	0.0122055
123514	Media_24_h	2016-08-04	NA
123515	Media_24_h	2016-08-05	NA
123516	Media_24_h	2016-08-06	NA

# most recent day
max(as.Date(fin[!is.na(fin$value), "date"]))

## [1] "2016-08-03"

addmargins(table(fin$inq, fin$tdm, useNA = "always")) %>% knitr::kable()

	Max_media_8h	Max_orario	Media_24_h	Sum
	0	2628	5256	7884
Benzene	0	18396	0	18396
CO	11826	11826	0	23652
NO2	0	14454	0	14454
O3	11826	11826	0	23652
PM10	0	0	13140	13140
SO2	0	10512	11826	22338
NA	0	0	0	0
Sum	23652	69642	30222	123516

Read the data

4 Overview

addmargins(table(fin$stazione, !is.na(fin$value), useNA= "always")) %>% knitr::kable()

	FALSE	TRUE	Sum
	13800	654	14454
boccetta	7152	732	7884
cda_gabbia	631	4625	5256
enna	1232	10594	11826
ex_autoparco	325	989	1314
lab_mobile1	8720	478	9198
megara	254	1060	1314
misterbianco	780	11046	11826
parcheggio_agip	308	1006	1314
partinico	558	11268	11826
porto_empedocle	11009	817	11826
sasol	517	797	1314
termica_milazzo	1285	7913	9198
termini	931	10895	11826
trapani	1230	10596	11826
villa_augusta	1012	302	1314
NA	0	0	0
Sum	49744	73772	123516

addmargins(with(fin[!is.na(fin$value), ], table(stazione, inq, useNA= "always"))) %>% knitr::kable()

		Benzene	CO	NO2	O3	PM10	SO2	Sum
	354	0	123	64	0	54	59	654
boccetta	0	0	0	138	278	40	276	732
cda_gabbia	0	1090	0	1183	0	0	2352	4625
enna	0	965	2529	1259	2472	1219	2150	10594
ex_autoparco	0	989	0	0	0	0	0	989
lab_mobile1	0	60	125	140	99	54	0	478
megara	0	1060	0	0	0	0	0	1060
misterbianco	0	1112	2551	1170	2483	1270	2460	11046
parcheggio_agip	0	1006	0	0	0	0	0	1006
partinico	0	1109	2582	1298	2481	1254	2544	11268
porto_empedocle	0	82	117	107	214	83	214	817
sasol	0	797	0	0	0	0	0	797
termica_milazzo	0	1128	1836	1292	2417	1240	0	7913
termini	0	1044	2529	1277	2526	1205	2314	10895
trapani	0	1138	2331	1224	2404	1149	2350	10596
villa_augusta	0	302	0	0	0	0	0	302
NA	0	0	0	0	0	0	0	0
Sum	354	11882	14723	9152	15374	7568	14719	73772

5 Export csv file (gzipped)

con_out <- gzfile("maria_report_04_long.csv.gz", open = "wb")
   write.csv(fin, file = con_out, row.names = FALSE)
close(con_out)

6 PM10

fin_pm10 <- subset(fin, subset = stazione %in% c("enna", "trapani", "partinico", 
                                                 "misterbianco", "termini", 
                                                 "termica_milazzo") & 
                        inq == "PM10" & tdm == "Media_24_h")
p1 <- ggplot(fin_pm10, aes(x=as.Date(date), y=value, col= stazione)) + geom_line()
p1 <- p1 + geom_smooth()
p1 <- p1 + facet_wrap( ~ stazione, nrow = 1)
p1 <- p1 + geom_hline(yintercept = 50, col = "blue")
p1 <- p1 + labs(x = "Date", y = "value")
p1 <- p1 + ma_theme()
p1

## Warning: Removed 547 rows containing non-finite values (stat_smooth).

## Warning: Removed 21 rows containing missing values (geom_path).

7 O3

fin_o3 <- subset(fin, subset = stazione %in% c("enna", "trapani", "partinico", 
                                               "misterbianco", "termini", 
                                               "termica_milazzo") & 
                        inq == "O3" & tdm == "Max_media_8h")
p1 <- ggplot(fin_o3, aes(x=as.Date(date), y=value, col= stazione)) + geom_line()
p1 <- p1 + geom_smooth()
p1 <- p1 + facet_wrap( ~ stazione, nrow = 1)
#p1 <- p1 + geom_hline(yintercept = 50, col = "blue")
p1 <- p1 + labs(x = "Date", y = "value")
p1 <- p1 + ma_theme()
p1

## Warning: Removed 495 rows containing non-finite values (stat_smooth).

## Warning: Removed 20 rows containing missing values (geom_path).

8 Number of missing observations by stazione, year and month (PM10)

missing_by_yr_month <- function(x) {
  z <- tbl_df(x) %>%
       dplyr::filter(is.na(value)) %>%
       mutate(yr = year(ymd(date)), month = month(ymd(date))) %>%
       select(stazione, yr, month) %>% 
       ungroup()
  with(z, table(yr, month, stazione))
}

(kx1 <- missing_by_yr_month(fin_pm10))

## , , stazione = enna
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  2  0  0  0  0  0  0  0  0  0  0  1
##   2014  0  0  3  0  0  0  1  2  3  6  0  4
##   2015  1  2  4 23  0  2  2  0  1  3  2  5
##   2016  6  4  0  4  4  7  0  3  0  0  0  0
## 
## , , stazione = misterbianco
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  1  0  1  0  0  0  0  0  0  1  1
##   2014  7  0  6  0  0  0  0  0  1  3  0  0
##   2015  0  0  1  1  0  0  3  2  0  2  1  5
##   2016  0  0  0  1  2  0  2  3  0  0  0  0
## 
## , , stazione = partinico
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  3  0  0  0  0  0  0  0  2  9  4  1
##   2014  8  1  0  0  1  0  1  0  1  0  0  0
##   2015  0  1  1  1  0  0  1  6  4  2  2  2
##   2016  2  0  1  0  2  0  0  4  0  0  0  0
## 
## , , stazione = termica_milazzo
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  1  0  0  0  0  0  3  0  0  0  1
##   2014  0  0  0  0  0  0  0  0  0  0  0  0
##   2015  0  0  0  0  0  2  0 10  0  0 17 11
##   2016  7  6  5  0  1  0  5  5  0  0  0  0
## 
## , , stazione = termini
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  2  0  0  0  0  0  0  0  0  1  4
##   2014  0  0  7  0  0  0  0  0  0  5 11 31
##   2015 21  0  1  0  0  1  1  2  2  0  4  1
##   2016  0  1  0  6  2  0  2  4  0  0  0  0
## 
## , , stazione = trapani
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  0  8 14  0  0  7 12  0  0  1  0
##   2014  0  0  3 30  5  1 13 16  7  3  0  0
##   2015 10  4  1  4  4  1  3  0  1  3  1  2
##   2016  0  0  1  3  4  0  0  3  0  0  0  0

# year 2016
addmargins(t(kx1["2016",1:12,]), 1)

##                  month
## stazione           1  2  3  4  5  6  7  8  9 10 11 12
##   enna             6  4  0  4  4  7  0  3  0  0  0  0
##   misterbianco     0  0  0  1  2  0  2  3  0  0  0  0
##   partinico        2  0  1  0  2  0  0  4  0  0  0  0
##   termica_milazzo  7  6  5  0  1  0  5  5  0  0  0  0
##   termini          0  1  0  6  2  0  2  4  0  0  0  0
##   trapani          0  0  1  3  4  0  0  3  0  0  0  0
##   Sum             15 11  7 14 15  7  9 22  0  0  0  0

# Trim observations from each end before the computation
trim_obs <- function(x, trim) {
  m <- c(quantile(x, probs = trim, na.rm = TRUE), 
         quantile(x, probs = 1 - trim, na.rm = TRUE))
  print(m)
  out <- (x >= m[1] & x <= m[2])
  return(out)
}

fin_pm10_trim <- tbl_df(fin_pm10) %>%
                 group_by(stazione) %>%
                 mutate(ixd = trim_obs(value, 0.5/100)) %>%
                 ungroup()

##     0.5%    99.5% 
##  0.15636 84.75051 
##     0.5%    99.5% 
##  5.08166 90.90610 
##     0.5%    99.5% 
##   5.4795 100.4106 
##     0.5%    99.5% 
##  7.49696 84.98849 
##     0.5%    99.5% 
##  1.18864 74.17688 
##     0.5%    99.5% 
##  6.07696 89.37207

table(fin_pm10_trim$stazione, fin_pm10_trim$ixd)

##                  
##                   FALSE TRUE
##   enna               14 1205
##   misterbianco       14 1256
##   partinico          14 1240
##   termica_milazzo    14 1226
##   termini            14 1191
##   trapani            12 1137

fin_pm10_trim <- dplyr::filter(fin_pm10_trim, ixd == TRUE)

p2 <- ggplot(fin_pm10_trim, aes(x=as.Date(date), y=value, col = stazione))
p2 <- p2 + geom_smooth()
p2 <- p2 + facet_wrap( ~ stazione, nrow = 1)
p2 <- p2 + labs(x = "Date", y = "value")
p2 <- p2 + ma_theme()
p2

fin_pm10_trim$date <- as.character(year(as.Date(fin_pm10_trim$date)))

p3 <- ggplot(fin_pm10_trim, aes(x = date, y = value, group = stazione, col = stazione))
p3 <- p3 + geom_boxplot(group = 2) + geom_jitter(width = 0.2, alpha = 0.35)
p3 <- p3 + facet_wrap( ~ stazione, nrow = 1)
p3 <- p3 + labs(x = "Date", y = "value")
p3 <- p3 + ma_theme()
p3

9 Highcharter

fin_hc <- tbl_df(fin) %>% 
          dplyr::filter(stazione %in% c("enna", "trapani", "termica_milazzo", 
                                        "partinico", "termini"), 
                        inq == "PM10", tdm == "Media_24_h") %>%
          mutate(year_month = format(as.Date(date), "%Y-%m")) %>%
          group_by(stazione, year_month) %>%
          summarise(mean_val = round(mean(value, na.rm = TRUE), 2)) %>%
          ungroup()

fin_hc <- data.frame(fin_hc)

hc1 <- highchart() %>% 
        hc_xAxis(categories = fin_hc$year_month) %>% 
        hc_add_series(name = "Enna", data = fin_hc[fin_hc$stazione=="enna", "mean_val"]) %>%
        hc_add_series(name = "Partinico", data = fin_hc[fin_hc$stazione=="partinico", "mean_val"]) %>%
        hc_add_series(name = "Termica Milazzo", data = fin_hc[fin_hc$stazione=="termica_milazzo", "mean_val"]) %>%
        hc_add_series(name = "Termini", data = fin_hc[fin_hc$stazione=="termini", "mean_val"]) %>%
        hc_add_series(name = "Trapani", data = fin_hc[fin_hc$stazione=="trapani", "mean_val"])

hc1

10 Vegalite

vegalite(export=TRUE) %>%
  cell_size(400, 400) %>% 
  add_data(fin_hc) %>%
  encode_x("year_month", "temporal") %>%
  encode_y("mean_val", "quantitative") %>%
  encode_color("stazione", "nominal") %>% 
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  mark_line() -> vl

vl

11 Dygraph

xdy <- fin_pm10[fin_pm10$stazione == "enna", ]
dygraph(xts(xdy$value, order.by = as.POSIXct(xdy$date)), 
            main = "Enna // Inquinante: PM10")  %>% 
        dyRangeSelector()

12 Dygraph, trimmed values

fin_pm10_trim <- tbl_df(fin_pm10) %>%
                 group_by(stazione) %>%
                 mutate(ixd = trim_obs(value, 0.5/100)) %>%
                 ungroup()

##     0.5%    99.5% 
##  0.15636 84.75051 
##     0.5%    99.5% 
##  5.08166 90.90610 
##     0.5%    99.5% 
##   5.4795 100.4106 
##     0.5%    99.5% 
##  7.49696 84.98849 
##     0.5%    99.5% 
##  1.18864 74.17688 
##     0.5%    99.5% 
##  6.07696 89.37207

fin_pm10_trim <- dplyr::filter(fin_pm10_trim, ixd == TRUE)

xdy1 <- fin_pm10_trim[fin_pm10_trim$stazione == "enna", ]
dygraph(xts(xdy1$value, order.by = as.POSIXct(xdy1$date)), 
            main = "Enna // Inquinante: PM10, trimmed values")  %>% 
        dyRangeSelector()

13 rbokeh

# Trimmed values, PM10, Enna

figure(width = 800, height = 600) %>%
       ly_hist(value, data = xdy1, breaks = 40, freq = FALSE) %>%
       ly_density(value, data = xdy1)

xdy2 <- fin_pm10_trim[fin_pm10_trim$stazione %in% c("enna", "termini"), ]

figure(legend_location = "top_left") %>%
  ly_quantile(value, group = stazione, data = xdy2)

14 Maximum per year (PM10)

tbl_df(fin) %>% 
          mutate(year = format(as.Date(date), "%Y")) %>%
          dplyr::filter(inq == "PM10", tdm == "Media_24_h") %>%
          group_by(stazione, year) %>%
          summarise(max_val = round(max(value, na.rm = TRUE), 2)) %>%
          ungroup() %>%
          spread(year, max_val) %>% knitr::kable()

stazione	2013	2014	2015	2016
	NA	NA	NA	76.90
boccetta	28.22	37.60	NA	NA
enna	81.86	91.95	101.20	216.60
lab_mobile1	NA	NA	NA	74.80
misterbianco	74.32	137.28	112.88	306.40
partinico	64.08	462.20	195.80	229.72
porto_empedocle	NA	NA	75.10	49.90
termica_milazzo	99.03	103.10	89.70	156.00
termini	62.62	135.90	70.30	292.70
trapani	61.04	183.87	74.50	141.60

15 Days per year with a PM10 value over 50 mg/m3 (max. 35 excedances allowed per year)

m <- tbl_df(fin) %>%
       dplyr::filter(inq == "PM10") %>%
       mutate(year = format(as.Date(date), "%Y")) %>%
       group_by(stazione, year) %>%
       mutate(days_gt50 = cumsum(ifelse(!is.na(value) & value > 50, 1, 0))) %>%
       select(stazione, year, days_gt50) %>%
       top_n(1, days_gt50)
       #distinct(stazione, year)

spread(unique(m), year, days_gt50) %>% knitr::kable()

stazione	2013	2014	2015	2016
	0	0	0	1
boccetta	0	0	0	0
enna	4	9	6	5
lab_mobile1	0	0	0	1
misterbianco	4	16	5	4
partinico	4	21	9	6
porto_empedocle	0	0	29	0
termica_milazzo	5	18	8	8
termini	2	9	6	6
trapani	2	12	1	4

16 Mappa stazioni

geo_stazioni <- read.csv("http://www.covimo.de/arpa/stazioni.csv")
leaflet(data = geo_stazioni[1:11, ]) %>% 
        addTiles() %>%
        addMarkers(~longitude, ~latitude, popup = ~as.character(Cartella))

devtools::session_info()

## Session info --------------------------------------------------------------

##  setting  value                                      
##  version  R version 3.3.1 Patched (2016-06-24 r70831)
##  system   x86_64, mingw32                            
##  ui       RTerm                                      
##  language (EN)                                       
##  collate  German_Germany.1252                        
##  tz       Europe/Berlin                              
##  date     2016-08-07

## Packages ------------------------------------------------------------------

##  package     * version    date       source                            
##  assertthat    0.1        2013-12-06 CRAN (R 3.3.1)                    
##  base64        2.0        2016-05-10 CRAN (R 3.3.1)                    
##  broom         0.4.1      2016-06-24 CRAN (R 3.3.1)                    
##  chron         2.3-47     2015-06-24 CRAN (R 3.3.1)                    
##  clipr         0.2.1      2016-06-23 CRAN (R 3.3.1)                    
##  codetools     0.2-14     2015-07-15 CRAN (R 3.3.1)                    
##  colorspace    1.2-6      2015-03-11 CRAN (R 3.3.1)                    
##  data.table    1.9.6      2015-09-19 CRAN (R 3.3.1)                    
##  DBI           0.4-1      2016-05-08 CRAN (R 3.3.1)                    
##  devtools      1.12.0     2016-06-24 CRAN (R 3.3.1)                    
##  digest        0.6.10     2016-08-02 CRAN (R 3.3.1)                    
##  dplyr       * 0.5.0      2016-06-24 CRAN (R 3.3.1)                    
##  dygraphs    * 1.1.1.0    2016-08-01 CRAN (R 3.3.1)                    
##  evaluate      0.9        2016-04-29 CRAN (R 3.3.1)                    
##  formatR       1.4        2016-05-09 CRAN (R 3.3.1)                    
##  ggplot2     * 2.1.0      2016-03-01 CRAN (R 3.3.1)                    
##  gistr         0.3.6      2015-12-22 CRAN (R 3.3.1)                    
##  gtable        0.2.0      2016-02-26 CRAN (R 3.3.1)                    
##  hexbin        1.27.1     2015-08-19 CRAN (R 3.3.1)                    
##  highcharter * 0.4.0      2016-07-15 CRAN (R 3.3.1)                    
##  highr         0.6        2016-05-09 CRAN (R 3.3.1)                    
##  htmltools     0.3.5      2016-03-21 CRAN (R 3.3.1)                    
##  htmlwidgets   0.7        2016-08-02 CRAN (R 3.3.1)                    
##  httr          1.2.1      2016-07-03 CRAN (R 3.3.1)                    
##  igraph        1.0.1      2015-06-26 CRAN (R 3.3.0)                    
##  jsonlite      1.0        2016-07-01 CRAN (R 3.3.1)                    
##  knitr         1.13       2016-05-09 CRAN (R 3.3.1)                    
##  labeling      0.3        2014-08-23 CRAN (R 3.3.0)                    
##  lattice       0.20-33    2015-07-14 CRAN (R 3.3.1)                    
##  lazyeval      0.2.0      2016-06-12 CRAN (R 3.3.1)                    
##  leaflet     * 1.0.1.9004 2016-06-26 Github (rstudio/leaflet@e2751ff)  
##  lubridate   * 1.5.6      2016-04-06 CRAN (R 3.3.1)                    
##  magrittr      1.5        2014-11-22 CRAN (R 3.3.1)                    
##  maps          3.1.1      2016-07-27 CRAN (R 3.3.1)                    
##  Matrix        1.2-6      2016-05-02 CRAN (R 3.3.1)                    
##  memoise       1.0.0      2016-01-29 CRAN (R 3.3.1)                    
##  mgcv          1.8-13     2016-07-21 CRAN (R 3.3.1)                    
##  mnormt        1.5-4      2016-03-09 CRAN (R 3.3.0)                    
##  munsell       0.4.3      2016-02-13 CRAN (R 3.3.1)                    
##  nlme          3.1-128    2016-05-10 CRAN (R 3.3.1)                    
##  openssl       0.9.4      2016-05-25 CRAN (R 3.3.1)                    
##  plyr          1.8.4      2016-06-08 CRAN (R 3.3.1)                    
##  pryr          0.1.2      2015-06-20 CRAN (R 3.3.1)                    
##  psych         1.6.6      2016-06-28 CRAN (R 3.3.1)                    
##  purrr         0.2.2      2016-06-18 CRAN (R 3.3.1)                    
##  quantmod      0.4-5      2015-07-24 CRAN (R 3.3.1)                    
##  R6            2.1.2      2016-01-26 CRAN (R 3.3.1)                    
##  rbokeh      * 0.4.2      2016-07-09 Github (bokeh/rbokeh@1a73a42)     
##  Rcpp          0.12.6     2016-07-19 CRAN (R 3.3.1)                    
##  reshape2      1.4.1      2014-12-06 CRAN (R 3.3.1)                    
##  rlist         0.4.6.1    2016-04-04 CRAN (R 3.3.1)                    
##  rmarkdown     1.0.9002   2016-08-06 Github (rstudio/rmarkdown@4f66e62)
##  scales        0.4.0      2016-02-26 CRAN (R 3.3.1)                    
##  stringi       1.1.1      2016-05-27 CRAN (R 3.3.0)                    
##  stringr     * 1.0.0      2015-04-30 CRAN (R 3.3.1)                    
##  tibble        1.1        2016-07-04 CRAN (R 3.3.1)                    
##  tidyr       * 0.5.1      2016-06-14 CRAN (R 3.3.1)                    
##  TTR           0.23-1     2016-03-21 CRAN (R 3.3.1)                    
##  vegalite    * 0.6.1      2016-06-26 Github (hrbrmstr/vegalite@8c31c1b)
##  viridisLite   0.1.3      2016-03-12 CRAN (R 3.3.1)                    
##  webshot       0.3.2      2016-06-23 CRAN (R 3.3.1)                    
##  withr         1.0.2      2016-06-20 CRAN (R 3.3.1)                    
##  xts         * 0.9-7      2014-01-02 CRAN (R 3.3.1)                    
##  yaml          2.1.13     2014-06-12 CRAN (R 3.3.1)                    
##  zoo         * 1.7-13     2016-05-03 CRAN (R 3.3.1)

Maria_report

Patrick Hausmann

1 Todo

2 Load some libraries

3 Import the data

4 Overview

5 Export csv file (gzipped)

6 PM10

7 O3

8 Number of missing observations by stazione, year and month (PM10)

9 Highcharter

10 Vegalite

11 Dygraph

12 Dygraph, trimmed values

13 rbokeh

14 Maximum per year (PM10)

15 Days per year with a PM10 value over 50 mg/m3 (max. 35 excedances allowed per year)

16 Mappa stazioni